Assessing students’ perceptions of the effects of a new Canadian longitudinal pre-clerkship family medicine experience
Bibliographic record
Abstract
BACKGROUND: Despite the implementation of longitudinal community-based pre-clerkship courses in several Canadian medical schools, there is a paucity of data assessing students' views regarding their experiences. The present study sought to measure students' perceived effects of the new Longitudinal Family Medicine Experience (LFME) course at McGill University. METHODS: A 34-item questionnaire called the 'LFME Survey (Student Version)' was created, and all first-year medical students completed it online. RESULTS: The participation rate was 64% (N = 120). Eight factors were identified in the factor analysis performed: overall satisfaction, satisfaction with preceptor, knowledge, affective learning, clinical skills, teaching/feedback, professional identity/professionalism and attitude toward primary care. Factor composite scores were above 4.5/7,indicating that students had positive perceptions of the LFME. Students felt that the LFME was a valuable educational experience and that their preceptors were good role-models. The course improved students' confidence, reinforced their commitment to being a physician and increased their positive attitude toward primary care. INTERPRETATION: Along with similar pre-clerkship courses, the LFME provides a valuable context for developing students' clinical skills, providing real-world cases, teaching patient-centred care and improving attitudes toward primary care. The LFME Survey appears to be a promising and innovative tool that deserves further validation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".